Cut Histograms

Cut Histograms address the situation where there is a large ``normal'' population of events surrounded by abnormal ones. The idea is to place a set of cuts and define an event as normal if it lies within all cuts and abnormal otherwise. Normal events just produce histograms and no n-tuples, abnormal events produce n-tuples but no histograms. In this way the full power of the n-tuple is kept for those events requiring futher study whilst still getting some information for the rest of the data set.

To select this mode of operation just add the letter C (for cut) at the end of one or more any HIST keywords e.g. HIST2C. Then the histogram limits also define the cut on the variable. Note: this has nothing to do with the VETO cut which is always applied first while assembling the event.